In order to obtain a large receptive field without increasing model parameters, recursive learning is proposed for SISR, where the same sub-modules are repeatedly applied in the network and they share the same parameters. In other worlds, a recursive block is a collection of recursive units, where the corresponding structures among these recursive units share the same parameters. For instance, the same convolutional layer is applied 16 times in DRCN [1]}, resulting in a 41 \(\times \)  41 size receptive field. However, too many stacked layers in recursive learning based model will still cause the problem of vanishing/exploding gradient. Therefore, in DRRN [2]}, the recursive block is conducted based on residual learning (Fig. REF ). Recently, more and more models introduce the residual learning strategy in their recursive units, such as MemNet [3]}, CARN [4]}, and SRRFN [5]}.
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